Question

我采用了标准的ResNet50模型：

model = keras.applications.resnet50.ResNet50(include_top=False, 
                                     weights='imagenet',
                                     classes=10,
                                     input_shape=(224, 224, 3))

并添加了我自己的几个密集层：

top_model = Sequential()
top_model.add(Flatten(input_shape=model.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(2, activation='softmax'))

model = Model(input=model.input, output=top_model(model.output))

这种方式效果很好，但是，当我想删除带有model.pop() keras的最后一个Dense和Dropout图层时效果不好：

model.layers[-1].layers
[<keras.layers.core.Flatten at 0x16b5c00b8>,
 <keras.layers.core.Dense at 0x16b5c0320>,
 <keras.layers.core.Dropout at 0x16b5c02e8>,
 <keras.layers.core.Dense at 0x16b5c0d68>]

model.layers[-1].pop()
model.layers[-1].pop()
model.layers[-1].layers

[<keras.layers.core.Flatten at 0x1ae6e5940>,
 <keras.layers.core.Dense at 0x1ae6e9e10>]

model.layers[-1].outputs = [model.layers[-1].layers[-1].output]
model.outputs = model.layers[-1].outputs
model.layers[-1].layers[-1].outbound_nodes = []

然后我只是编译模型，在尝试预测时，我收到一个错误：

You must feed a value for placeholder tensor 'flatten_7_input_12' with dtype float and shape [?,1,1,2048]

Answer 1

model.pop()负责所有基础设置，包括将model.output设置为新最后一层的输出。因此，您不需要处理有关输出的任何事情另请注意，您要分配model变量;因此，model.outputs已经指的是扩展模型的输出。

以下示例代码在keras 2.0.6上使用TensorFlow后端（1.4.0）正常工作：

import keras
from keras.models import Sequential, Model
from keras.layers import *
import numpy as np

model = keras.applications.resnet50.ResNet50(include_top=False, 
                                 weights='imagenet',
                                 classes=10,
                                 input_shape=(224, 224, 3))

top_model = Sequential()
top_model.add(keras.layers.Flatten(input_shape=model.output_shape[1:]))
top_model.add(keras.layers.Dense(256, activation='relu'))
top_model.add(keras.layers.Dropout(0.5))
top_model.add(keras.layers.Dense(2, activation='softmax'))

model_extended = Model(input=model.input, output=top_model(model.output))
model_extended.layers[-1].pop()
model_extended.layers[-1].pop()
model_extended.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model_extended.predict(np.zeros((1, 224, 224, 3)))

keras - 模型不会在pop（）之后工作

1 个答案: